
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__     :		Optional[int]        = logging.get_logger(__name__)

lowerCamelCase__     :		Any        = {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__     :		Tuple        = {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

lowerCamelCase__     :		int        = {
    """camembert-base""": 5_1_2,
}

lowerCamelCase__     :		int        = """▁"""



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[Any]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  str	   =      ['input_ids', 'attention_mask']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="<mask>"       ,	SCREAMING_SNAKE_CASE_=["<s>NOTUSED", "</s>NOTUSED"]       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Dict     		=   AddedToken(SCREAMING_SNAKE_CASE_       ,	lstrip=SCREAMING_SNAKE_CASE_       ,	rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else mask_token

   lowercase__	: int     		=   {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=SCREAMING_SNAKE_CASE_       ,	eos_token=SCREAMING_SNAKE_CASE_       ,	unk_token=SCREAMING_SNAKE_CASE_       ,	sep_token=SCREAMING_SNAKE_CASE_       ,	cls_token=SCREAMING_SNAKE_CASE_       ,	pad_token=SCREAMING_SNAKE_CASE_       ,	mask_token=SCREAMING_SNAKE_CASE_       ,	additional_special_tokens=SCREAMING_SNAKE_CASE_       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: Tuple     		=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   vocab_file
   # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
   # sentencepiece vocabulary (this is the case for <s> and </s>
   lowercase__	: int     		=   {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
   lowercase__	: Union[str, Any]     		=   len(self.fairseq_tokens_to_ids)
   lowercase__	: Any     		=   len(self.sp_model) + len(self.fairseq_tokens_to_ids)
   lowercase__	: Union[str, Any]     		=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowercase__	: Tuple     		=   [self.cls_token_id]
   lowercase__	: Dict     		=   [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = False):


   '''simple docstring'''




   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=SCREAMING_SNAKE_CASE_       ,	token_ids_a=SCREAMING_SNAKE_CASE_       ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_)

   if token_ids_a is None:
     return [1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1]
   return [1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1]


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self.sep_token_id]
   lowercase__	: Dict     		=   [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return len(self.fairseq_tokens_to_ids) + len(self.sp_model)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.sp_model.encode(SCREAMING_SNAKE_CASE_       ,	out_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   elif self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
   return self.fairseq_offset + self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   []
   lowercase__	: Optional[Any]     		=   """"""
   lowercase__	: List[Any]     		=   False
   for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       if not prev_is_special:
         out_string += " "
       out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_) + token
       lowercase__	: List[str]     		=   True
       lowercase__	: List[Any]     		=   []
     else:
       current_sub_tokens.append(SCREAMING_SNAKE_CASE_)
       lowercase__	: Optional[int]     		=   False
   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_)
   return out_string.strip()


 def __getstate__(       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.__dict__.copy()
   lowercase__	: int     		=   None
   return state


 def __setstate__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   d

   # for backward compatibility
   if not hasattr(self       ,	"""sp_model_kwargs"""):
     lowercase__	: Union[str, Any]     		=   {}

   lowercase__	: Optional[int]     		=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if not os.path.isdir(SCREAMING_SNAKE_CASE_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   lowercase__	: Optional[int]     		=   os.path.join(
       SCREAMING_SNAKE_CASE_       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

   if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file       ,	SCREAMING_SNAKE_CASE_)
   elif not os.path.isfile(self.vocab_file):
     with open(SCREAMING_SNAKE_CASE_       ,	"""wb""") as fi:
       lowercase__	: Tuple     		=   self.sp_model.serialized_model_proto()
       fi.write(SCREAMING_SNAKE_CASE_)

   return (out_vocab_file,)










'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= tempfile.mkdtemp()

											# fmt: off
											_SCREAMING_SNAKE_CASE					:			Any							= ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
											# fmt: on
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= dict(zip(__snake_case       ,   range(len(__snake_case    )    )    )    )
											_SCREAMING_SNAKE_CASE					:			str							= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											_SCREAMING_SNAKE_CASE					:			Any							= {"""unk_token""": """<unk>"""}

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""vocab_file"""]    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""merges_file"""]    )
											with open(self.vocab_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(__snake_case    ) + """\n"""    )
											with open(self.merges_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(__snake_case    )    )

											_SCREAMING_SNAKE_CASE					:			Any							= {
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
											    """image_std""": [0.26862954, 0.26130258, 0.27577711],
											}
											_SCREAMING_SNAKE_CASE					:			List[str]							= os.path.join(self.tmpdirname       ,   __snake_case    )
											with open(self.image_processor_file       ,   """w"""       ,   encoding="""utf-8"""    ) as fp:
															json.dump(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return ViTImageProcessor.from_pretrained(self.tmpdirname       ,   **__snake_case    )






							def UpperCAmelCase_      (       self    ):
											shutil.rmtree(self.tmpdirname    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta    )]

											_SCREAMING_SNAKE_CASE					:			List[str]							= [Image.fromarray(np.moveaxis(__snake_case       ,   0       ,   -1    )    ) for x in image_inputs]

											return image_inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_tokenizer()
											_SCREAMING_SNAKE_CASE					:			int							= self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()

											_SCREAMING_SNAKE_CASE					:			str							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_slow.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= CLIPSegProcessor.from_pretrained(self.tmpdirname       ,   use_fast=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )
											processor_fast.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor.from_pretrained(self.tmpdirname    )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,   tokenizer_slow.get_vocab()    )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertEqual(tokenizer_slow.get_vocab()       ,   tokenizer_fast.get_vocab()    )
											self.assertIsInstance(processor_slow.tokenizer       ,   __snake_case    )
											self.assertIsInstance(processor_fast.tokenizer       ,   __snake_case    )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,   image_processor.to_json_string()    )
											self.assertIsInstance(processor_slow.image_processor       ,   __snake_case    )
											self.assertIsInstance(processor_fast.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,   image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											_SCREAMING_SNAKE_CASE					:			str							= self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor(do_normalize=__snake_case       ,   padding_value=1.0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= CLIPSegProcessor.from_pretrained(
											    self.tmpdirname       ,   bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       ,   do_normalize=__snake_case       ,   padding_value=1.0    )

											self.assertEqual(processor.tokenizer.get_vocab()       ,   tokenizer_add_kwargs.get_vocab()    )
											self.assertIsInstance(processor.tokenizer       ,   __snake_case    )

											self.assertEqual(processor.image_processor.to_json_string()       ,   image_processor_add_kwargs.to_json_string()    )
											self.assertIsInstance(processor.image_processor       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_processor(__snake_case       ,   return_tensors="""np"""    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= processor(images=__snake_case       ,   return_tensors="""np"""    )

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()       ,   input_processor[key].sum()       ,   delta=1e-2    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= """lower newer"""

											_SCREAMING_SNAKE_CASE					:			Any							= processor(text=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= tokenizer(__snake_case    )

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Dict							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= """lower newer"""
											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[str]							= processor(text=__snake_case       ,   images=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""input_ids""", """attention_mask""", """pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= self.prepare_image_inputs()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= processor(images=__snake_case       ,   visual_prompt=__snake_case    )

											self.assertListEqual(list(inputs.keys()    )       ,   ["""pixel_values""", """conditional_pixel_values"""]    )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case    ):
															processor()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_image_processor()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= CLIPSegProcessor(tokenizer=__snake_case       ,   image_processor=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_SCREAMING_SNAKE_CASE					:			int							= processor.batch_decode(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= tokenizer.batch_decode(__snake_case    )

											self.assertListEqual(__snake_case       ,   __snake_case    )




